A wide variety of interferometric imaging techniques have been developed to provide high resolution structural information in a wide range of applications. Optical Coherence Tomography (OCT) is a technique for performing high-resolution cross-sectional imaging that can provide images of samples including tissue structure on the micron scale in situ and in real time. OCT is an interferometric imaging method that determines the scattering profile of a sample along the OCT beam by detecting light reflected from a sample combined with a reference beam. Each scattering profile in the depth direction (z) is called an axial scan, or A-scan. Cross-sectional images (B-scans), and by extension 3D volumes, are built up from many A-scans, with the OCT beam moved to a set of transverse (x and y) locations on the sample. Many variants of OCT including time-domain OCT (TD-OCT), frequency domain or Fourier-Domain OCT (FD-OCT) (including spectral-domain OCT (SD-OCT) and swept-source OCT (SS-OCT)), have been developed employing different combinations of light sources, scanning configurations, and detection schemes. In parallel techniques, a series of spots, a line of light (line-field), or a two-dimensional array of light (full-field or partial field) are directed to the sample. The resulting reflected light is combined with reference light and detected. Parallel techniques can be accomplished in TD-OCT, SD-OCT or SS-OCT configurations. The related fields of optical diffraction tomography, holoscopy, digital interference holography, holographic OCT, and interferometric synthetic aperture microscopy are also interferometric imaging techniques that can be accomplished in point scanning and parallel configurations.
Interferometric imaging techniques have been applied extensively in the field of ophthalmology. OCT systems are able to image the various layers of the retina of the eye. FIG. 1 illustrates an OCT B-scan image 100 representing various canonical retinal layers and boundaries, which may include the inner limiting membrane (ILM) (as indicated by reference numeral 102), the retinal nerve fiber layer (RNFL or NFL) (as indicated by reference numeral 104), the ganglion cell layer (GCL) (as indicated by reference numeral 106), the inner plexiform layer (IPL) (as indicated by reference numeral 108), the inner nuclear layer (INL) (as indicated by reference 110), the outer plexiform layer (OPL) (as indicated by reference numeral 112), the outer nuclear layer (ONL) (as indicated by reference numeral 114), and the junction between the outer segments (OS) and inner segments (IS), as indicated by reference numeral 116, of the photoreceptors. The external or outer limiting membrane (ELM or OLM) (indicated by reference numeral 118) is the layer between the nuclei of the photoreceptors and their inner segments. Reference numerals 120 and 122 represent the retinal pigment epithelium (RPE) and bruch's membrane (BM), respectively. The purpose of segmentation in OCT imaging of the retina can be one of the following: (1) to detect a boundary between two structures, as in the junction between the inner and outer segments (IS/OS), which typically appears as a bright line in the OCT image (see for example, Jonnal et al. 2014, ‘The Cellular Origins of the Outer Retinal Bands in Optical Coherence Tomography Images,’ IOVS 55, 7904-7918, hereby incorporated by reference), or (2) to detect a layer that is represented as a single location, such as the RPE, which again appears as a bright line in the OCT image, or (3) to detect a layer with axial extent, such as the RNFL, which will have an inner boundary (which will be anatomically the border between the RNFL and the inner limiting membrane, but may be detected just as the ILM itself) and an outer boundary (which will be anatomically the border between the nerve fiber axon bundles and the nuclei in the ganglion cell layer). There is much overlap in these cases, as the RPE for some purposes may be considered to have an axial extent that may be associated with disease, but in most cases it is treated as a single axial location which determines the outer boundary of the retinal tissue. Segmentation of a layer that has an axial extent by definition implies identification of the inner boundary and outer boundary of that layer.
Pathologies of the eye are often present as structural and intensity modifications of the affected area in the OCT images. Further, because of the functional specificity of the various layers of the retina, different pathologies may affect only a specific subset of the various layers, while sparing the rest of the retinal constitution. This results in a change in a spatial relationship between various retinal layers, and quantifying this change is often a good indicator of an evolving or developed ocular pathology. Changes to thicknesses of retinal layers can also be used to identify and monitor various pathologies. Vital to establishing the extent and progression of retinal diseases is the ability to separate or segment the various retinal structures such as retinal layers, boundaries, and anatomical structures from pathologies.
Segmentation is the partitioning of an image into parts that are coherent according to some criteria, such as being anatomically related. When considered as a classification task, the purpose of segmentation is to assign labels to individual pixels or voxels within the image data. Some segmentation approaches (e.g., neural-based) perform segmentation 1) directly on the pixel data, 2) by preprocessing the intensity data, and/or 3) by using local structures provided to a classifier. Segmentation is a non-trivial operation requiring substantial computational resources. Often it is performed in a serial manner—beginning with an obvious structure (e.g., the ILM) and, with assumptions, additional structures are then located and segmented. A review of segmentation approaches is given in DuBuc D. C. 2011, Chap. 2, in Image Segmentation, Ed. P.-G. Ho, Pub: InTech, 15-54, hereby incorporated by reference.
In order to make OCT datasets clinically useful it is necessary to analyze the structure by segmentation of layers. However, due to the sheer amount of data, it is inconvenient or even impossible for a human operator to manually perform the segmentation in a high throughput clinical environment. Therefore, it is necessary to develop effective computer algorithms for automated segmentation of relevant layers of the investigated tissue, especially ones that can deliver results in a real-time or near real-time environment.
Existing published approaches to retinal OCT data segmentation vary depending on the number of layers to be segmented and on their robustness in the presence of strong speckle noise, shadows, irregularities (i.e. vessels, structural changes at the fovea and/or optic nerve head) and pathological changes in the tissue. (see for example, Fabritius, Tapio, et al. “Automated segmentation of the macula by optical coherence tomography.” Optics express 17.18 (2009): 15659-15669; Zawadzki, Robert J., et al. “Adaptive optics-optical coherence tomography: optimizing visualization of microscopic retinal structures in three dimensions.” JOSA A 24.5 (2007): 1373-1383; Garvin, Mona K., et al. “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search.” IEEE transactions on medical imaging 27.10 (2008): 1495-1505; Camilus & Govindan 2012, ‘A Review of Graph Based Segmentation,’ MECS, Ind. J. Graphics and Signal Proc. 5, 1-13; Fernandez, Delia Cabrera, Harry M. Salinas, and Carmen A. Puliafito. “Automated detection of retinal layer structures on optical coherence tomography images.” Optics Express 13.25 (2005): 10200-10216; Mujat, Mircea, et al. “Retinal nerve fiber layer thickness map determined from optical coherence tomography images.” Optics Express 13.23 (2005): 9480-9491; Koozekanani, Dara, Kim Boyer, and Cynthia Roberts. “Retinal thickness measurements from optical coherence tomography using a Markov boundary model.” IEEE transactions on medical imaging 20.9 (2001): 900-916; Tolliver, D. A., et al. “Automatic multiple retinal layer segmentation in spectral domain oct scans via spectral rounding.” Investigative Ophthalmology & Visual Science 49.13 (2008): 1878-1878; and Mishra, Akshaya, et al. “Intra-retinal layer segmentation in optical coherence tomography images.” Optics express 17.26 (2009): 23719-23728, the contents of each of which are hereby incorporated by reference). In general, segmentations tend to be very sensitive to noisy data or are limited only to a small number of layers. The previously mentioned segmentation approaches are beset with one or more of the following disadvantages: 1) they distinguish only the most prominent layers, 2) do not exhibit robustness in noisy and varied cases, 3) often require manual intervention of the operator; 4) are specific to a subset of retinal layers, 5) cannot deal with the range of retinal pathologies that could be present, 6) are computationally and execution-time intense, and 7) often require a classification system based upon an external reference database. Moreover, they are static systems in an application that requires dynamic decisions.